Nothing
```{asis, echo = {{envs_worldclim_knit}}, eval = {{envs_worldclim_knit}}, include = {{envs_worldclim_knit}}}
Using WorldClim v2.1 (http://www.worldclim.org/) bioclimatic dataset at resolution of r {{wcRes_rmd}}
arcmin.
```r}, include = {{envs_worldclim_knit}}} # Download environmental data envs_{{spAbr}} <- envs_worldclim( bcRes = {{wcRes_rmd}}, bcSel = {{bcSel_rmd}}, mapCntr = {{mapCntr_rmd}}, # Mandatory for 30 arcsec resolution doBrick = {{wcBrick_rmd}}) occs_xy_{{spAbr}} <- occs_{{spAbr}}[c('longitude', 'latitude')] occs_vals_{{spAbr}} <- as.data.frame(raster::extract(envs_{{spAbr}}, occs_xy_{{spAbr}}, cellnumbers = TRUE)) # Remove duplicated same cell values occs_{{spAbr}} <- occs_{{spAbr}}[!duplicated(occs_vals_{{spAbr}}[, 1]), ] occs_vals_{{spAbr}} <- occs_vals_{{spAbr}}[!duplicated(occs_vals_{{spAbr}}[, 1]), -1] # remove occurrence records with NA environmental values occs_{{spAbr}} <- occs_{{spAbr}}[!(rowSums(is.na(occs_vals_{{spAbr}})) >= 1), ] # also remove variable value rows with NA environmental values occs_vals_{{spAbr}} <- na.omit(occs_vals_{{spAbr}}) # add columns for env variable values for each occurrence record occs_{{spAbr}} <- cbind(occs_{{spAbr}}, occs_vals_{{spAbr}})
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